Dataset Information

Highly sensitive feature detection for high resolution LC/MS

ABSTRACT:

BACKGROUND: Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory.

RESULTS: We developed a new feature detection algorithm centWave for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity.

CONCLUSION: The new feature detection algorithm meets the requirements of current metabolomics experiments. centWave can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter (the original algorithm of XCMS) and the centroidPicker from MZmine. The centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from (http://www.bioconductor.org/).

INSTRUMENT(S): micrOTOF-Q ESI-Qq-TOF (Bruker), micrOTOF-Q ESI TOF (Bruker)

SUBMITTER: Steffen Neumann

PROVIDER: MTBLS1430 | MetaboLights | 2020-01-15

REPOSITORIES: MetaboLights

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Dataset's files

Source:

			Action	DRS
	MTBLS1430	Other
	FILES	Other
	a_MTBLS1430_sampleSet-1_metabolite_profiling_mass_spectrometry.txt	Txt
	a_MTBLS1430_sampleSet-2_metabolite_profiling_mass_spectrometry.txt	Txt
	a_MTBLS1430_sampleSet-3_metabolite_profiling_mass_spectrometry.txt	Txt

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Publications

Highly sensitive feature detection for high resolution LC/MS.

Tautenhahn Ralf R Böttcher Christoph C Neumann Steffen S

BMC bioinformatics 20081128

<h4>Background</h4>Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory.<h4>Results</h4>We ...[more]

PMID: 19040729

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Project description:Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine–based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems. Simulated data is reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS235' rel='noopener noreferrer' target='_blank'>MTBLS235</a>.Plasma data is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS234' rel='noopener noreferrer' target='_blank'>MTBLS234</a>.

Project description:Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine–based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems. Plasma data is reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS234' rel='noopener noreferrer' target='_blank'>MTBLS234</a>.Simulated data is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS235' rel='noopener noreferrer' target='_blank'>MTBLS235</a>.